What does word2vec actually learn? Berkeley AI Research offers a closed-form theoretical explanation
What exactly does word2vec learn?
What happened
Berkeley AI Research proves that under a fourth-order approximate objective, specific hyperparameter constraints, tiny initial embedding weights, and tiny gradient-descent steps, word2vec's learning problem reduces to an unweighted least-squares matrix factorization. Its gradient-flow dynamics have a closed-form solution, and the resulting representations are equivalent to applying PCA to matrix M*.
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